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Models.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Mar 19 15:35:50 2023
@author: Yousha
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from dython import nominal
df = pd.read_csv('data/naukri_final_data.csv')
df.columns
df.drop('Unnamed: 0',axis=1,inplace=True)
columns= ['avg_experience','title', 'company', 'ratings', 'reviews', 'experience', 'salary',
'location', 'days_posted', 'tags', 'job_simp', 'post','top_20_comp',
'salary_mentioned', 'reviews_int', 'min_experience', 'max_experience',
'days_posted_int', 'bangalore', 'delhi', 'kolkata',
'mumbai', 'remote', 'gurgaon', 'hyderabad', 'noida', 'pune', 'python',
'sql', 'deep_learning', 'big_data', 'r', 'nlp', 'sas', 'git',
'tensorflow', 'pytorch', 'tableau', 'power_bi', 'apache', 'c++'
]
df = df.reindex(columns, axis='columns')
#cont_corr
plt.figure(figsize=(25,25))
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
sns.heatmap(df.corr(),annot=True,square=True,linewidth=2,cbar_kws={'shrink':0.823})
plt.savefig('plots/Cont_correlation.png',dpi=800,bbox_inches='tight')
# avg_experience
avg_exp = pd.DataFrame(df.corr()['avg_experience'].sort_values(ascending=False))
plt.figure(figsize=(25,25))
sns.heatmap(avg_exp,square=True,linewidth=2,cbar=True,annot=True,annot_kws={'rotation':90})
plt.xticks(fontsize=12,rotation=90)
plt.yticks(fontsize=12,rotation=130)
plt.savefig('plots/avg_exp_correlation.png',dpi=800,bbox_inches='tight')
features = ['avg_experience','ratings','salary','job_simp', 'post','top_20_comp',
'salary_mentioned', 'reviews_int','days_posted_int', 'bangalore', 'delhi',
'kolkata','mumbai', 'remote', 'gurgaon', 'hyderabad', 'noida', 'pune', 'python',
'sql', 'deep_learning', 'big_data', 'r', 'nlp', 'sas', 'git',
'tensorflow', 'pytorch', 'tableau', 'power_bi', 'apache', 'c++'
]
ax = plt.figure()
ax = nominal.associations(df[features],figsize=(20,20),title='Feature Correlations')
plt.show()
X = pd.get_dummies(df[features].drop('avg_experience',axis=1))
y = df.avg_experience.values
# Models
from sklearn.model_selection import cross_val_score
# train test split
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.2,random_state=1)
# statsmodel LR
import statsmodels.api as sm
X_sm = X = sm.add_constant(X)
model = sm.OLS(y,X_sm)
model.fit().summary()
# sklearn LR
from sklearn.linear_model import LinearRegression, Lasso
lr = LinearRegression()
np.mean(cross_val_score(lr, X_train, y_train, scoring='neg_mean_absolute_error',cv=3)) # -2.37
lr.fit(X_train,y_train)
y_pred = lr.predict(X_test)
pred_real = np.concatenate([y_pred.reshape(-1,1),y_test.reshape(-1,1)],axis=1)
residuals = y_test - y_pred
plt.scatter(np.linspace(0,residuals.max(),105), residuals,c=residuals,cmap='magma', edgecolors='black', linewidths=.1)
plt.colorbar(label="Quality", orientation="vertical")
# plot a horizontal line at y = 0
plt.hlines(y = 0,
xmin = 0, xmax=12.08,
linestyle='--',colors='black')
# set xlim
plt.xlim((0, 12.08))
plt.show()
# Lasso
lm = Lasso()
np.mean(cross_val_score(lm, X_train, y_train, scoring='neg_mean_absolute_error',cv=3)) # -2.22
alpha = []
error = []
for i in range(1,100):
lml = Lasso(alpha=i/100)
alpha.append(i/100)
x = np.mean(cross_val_score(lml, X_train, y_train, scoring='neg_mean_absolute_error',cv=3))
error.append(x)
plt.plot(alpha,error)
alph_err = tuple(zip(alpha,error))
err_df = pd.DataFrame(alph_err, columns=['Alpha','Error'])
best_lm = err_df.query('Error == Error.max()') #-2.14
lm = Lasso(alpha=0.09)
lm.fit(X_train,y_train)
y_pred_lm = lm.predict(X_test)
residuals_lm = y_test - y_pred_lm
plt.scatter(np.linspace(0,residuals_lm.max(),105), residuals_lm,c=residuals_lm,cmap='magma', edgecolors='black', linewidths=.1)
plt.colorbar(orientation="vertical")
plt.ylabel('Residuals')
plt.title('Lasso Regression',pad=15)
# plot a horizontal line at y = 0
plt.hlines(y = 0,
xmin = 0, xmax=residuals_lm.max(),
linestyle='--',colors='black')
# set xlim
plt.xlim((0, residuals_lm.max()))
plt.savefig('plots/Residuals.png',dpi=600,bbox_inches='tight')
plt.show()
# Random forest
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor()
np.mean(cross_val_score(rf, X_train, y_train, scoring='neg_mean_absolute_error',cv=3)) # -2.21
rf.fit(X_train,y_train) #performs better on unscaled data
y_pred_rf = rf.predict(X_test)
# GridSearch
from sklearn.model_selection import GridSearchCV
params = {'n_estimators': range(100,1000,100),
'criterion':('squared_error', 'absolute_error'),
'max_features':('auto','sqrt','log2')}
gs = GridSearchCV(rf, param_grid=params,scoring='neg_mean_absolute_error', cv=3)
gs.fit(X_train,y_train)
gs.best_score_
gs.best_params_
# Pickling
lm2 = Lasso(alpha=0.09)
lm2.fit(X,y)
import pickle
pickl = {'model':lm2}
pickle.dump(pickl, open('model_file'+'.p','wb'))
with open('model_file.p', 'rb') as pickled:
data = pickle.load(pickled)
model = data['model']
model.predict(X_test.iloc[1,:].values.reshape(1,-1))
list(X_test.iloc[1,:])